This study enhances the performance of global navigation satellite system reflectometry (GNSS-R) based sea ice classification using a machine learning (ML) model with both long short-term memory (LSTM) and neural network (NN) layers. Classification features are derived from Fengyun-3E (FY-3E) GNSS-R data and validated against sea ice labels from National Snow and Ice Data Center (NSIDC) data. Classifiers are trained to identify first-year ice (FYI), multi-year ice (MYI), and thin ice (TI). Training and testing data are temporally separated to evaluate model generalization. The proposed LSTM-NN architecture is evaluated for performance impact relative to standard NN and LSTM methods. The best performing LSTM-NN model yielded an overall accuracy of 80.00%. The proposed model achieves superior performance compared to the baseline NN model, which achieved an accuracy of 75.89%.

This paper presents the findings related to the design solution options for a next-generation C-band Synthetic Aperture Radar (SAR) mission, developed to address the Harmonized User Needs (HUN) in Earth observation (EO) data as defined by several departments of the Government of Canada. The work analyses various mission solution options, including multi-satellite constellations, and their performance to evaluate feasibility and assess their compliance with the HUN as well as minimize the associated lifecycle costs, technical risks, implementation schedule, and programmatic challenges. This mission concept contributes to the advancement of space-based surveillance solutions aligned with Canada’s long-term strategic objectives to ensure service continuity for Earth Observation and national security applications. Systematic user needs analysis helped to reveal the importance of high-resolution (1–5 m), enhanced interferometric, polarimetric SAR interferometry (PolInSAR) and other capabilities. Two satellite constellation configurations are proposed: (1) a three-medium-satellite setup with a tandem pair, and (2) a five-large-satellite system incorporating tandem and optimal orbits. Employing High-Resolution Wide Swath (HRWS) imaging modes and full polarimetric capability. Performance simulations indicate low Noise Equivalent Sigma Zero (NESZ) with wide swath width fully addresses driving needs for sea ice and ocean monitoring, covering most of the Canadian areas of interest, with the revisit time of less than 4–6 hours. Orbit optimization ensures high revisit rates, enabling novel interferometric SAR (InSAR) capabilities with observations separated by only a few hours. This mission concept, considering two options with three medium and with five large satellites, respectively, offers a flexible, scalable, and strategically impactful solution for Earth Observation (EO) service continuity and technological leadership for Canada until 2050 and beyond.

A deployable array antenna for the Killick-1 2U CubeSat equipped with a Global Navigation Satellite Systems Reflectometry (GNSS-R) payload is designed and tested. Considering the physical constraints, a low-profile array antenna consisting of six patch antennas spanned over three separate panels has been designed. To design the array antenna with lefthand circular polarization (LHCP), first, a single panel hosting two double-fed patches is designed and optimized using the Ansys HFSS software. Based on the single antenna panel, the six-element antenna array is modeled, fabricated, and tested. The test results indicate that with a 10.2 dBi gain, the proposed antenna array meets all the requirements. The proposed array antenna is a flexible and reliable solution for similar missions.

Spaceborne synthetic aperture radar (SAR) is an important technology for ship detection applications. It can provide timely information on small boat locations, which is important for security and safety applications. This letter describes the advantages of using very-high-resolution TerraSAR-X data acquired in staring spotlight mode for detecting small boats. Coincident to the SAR image acquisitions, electro-optical (EO) satellite imagery was used, together with field photographs of boats. The results demonstrate the ability to distinguish SAR signatures of small wooden and fiberglass vessels with the size of up to 4 m in length.

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands.

The need to monitor ice conditions has motivated the launch of several earth observation (EO) satellites and ice mapping applications are among the highest consumers of satellite data. However, oil and gas operations (O&G) in ice-prone (both sea ice and iceberg) environments have largely been using EO data for upstream, strategic reports on ice conditions. There are many recent and upcoming advances in EO technology that are already enabling satellites to be used for other critical operations and there is value in using satellites extensively for ice management. The remainder of this paper briefly describes these advances and their impact on detecting ice conditions to support oil and gas operations.

This article investigates and compares polarimetric signatures of icebergs embedded in sea ice and icebergs in open water. The main objective is to study on the backscatter properties of melting iceberg and to check on whether there is any distinguishable property in them in the case of different background clutter conditions (i.e. sea ice and open water). This study results will improve the potential of iceberg detection using radar polarimetry. RADARSAT-2 images have been used for the analysis acquired over locations near the coastline (approximately 3–35 km) of the island of Newfoundland. For analysis, polarimetry parameters, such as co-(HH) and cross-(HV) polarization and several popular decomposition techniques, specifically Pauli, Freeman–Durden, Yamaguchi, Cloud–Pottier, and van Zyl, have been used to determine the polarimetric signatures of icebergs and sea ice. The statistical hypothesis T-test has been applied to achieve a precise comparison among backscatters from different icebergs groups. Statistical results tend to show a dominant surface scattering mechanism for icebergs in all types of clutter conditions. Moreover, icebergs in open water produce larger volume scatter than icebergs in sea ice, whereas icebergs in sea ice produce larger surface scatter than icebergs in open water.

Ship detection and classification in very high resolution (VHR) EO/IR satellite imagery, as primary objectives, were investigated using multiple techniques. Automated algorithms were developed and their performance was evaluated using different satellite image sources (Pleiades, WorldView-2/3). Performance of ship detection algorithms based on traditional (thresholding and saliency) techniques reached probability of detection 80% for low false alarm rates. Deep learning techniques based on convolutional neural networks (CNNs) (YOLOv4 and Mask R-CNN) achieved average precision of 94–95% with 3% of false positives without the need of accurate land and cloud masking. Mask R-CNN also allows accurate determining ship size parameters. The problem of ship and non-ship classification was investigated using traditional and CNN based techniques. Linear Discriminant Analysis, Support Vector Machines and combined classifiers achieved classification accuracies close to 80–90%. At the same time, the usage of a technique based on GoogleNet CNN achieved 99% classification accuracy for ship, small boats and background targets.

Summary Ships and icebergs are similar in size and intensity in SAR images, so it is difficult to distinguish them in remote sensing images. Deep learning is a technique based on neural networks, which has played an important role in image information processing. In order to address the challenge of ship and iceberg classification, we present a convolutional neural network (CNN) based classification method for iceberg and ship discrimination from Sentinel‐1 SAR images with different polarizations and incidence angles. The method is based on the fixed constant false alarm rate (CFAR) detector and the CNN model has three input channels, then the model was trained using parallel algorithm. The CNN is trained using 1443 images and tested using 161 images. The CNN model is also compared with support vector machine (SVM) and k nearest neighbors (kNN) using the same dataset. Comparison shows the CNN‐based method performs the best, and it achieved a validation accuracy of 96%.

Flash floods can cause massive damages because of their rapid evolution. To reduce or prevent harm caused by a flash flood, it is vital to have information about its formation and spread. Hence, providing real-time surveillance flood is essential. Considering Hurricane Harvey and Hurricane Irma as two case studies, six different data preparation approaches (DPAs) for flood detection based on the Cyclone Global Navigation Satellite System (CYGNSS) data and the Random Under-Sampling Boosted (RUSBoost) classification algorithm are investigated in this article. Taking flood and land as two classes, flash flood detection is tackled as a binary classification problem. Eleven observables are extracted from the delay-Doppler maps (DDMs) for feature selection. These observables, alongside two features from an ancillary data, are considered in feature selection. All the combinations of these observables with and without ancillary data are fed into the classifier with 5-fold cross-validation one by one. Based on the test results, five observables with the ancillary data are selected as a suitable feature vector for flood detection here. Using the selected feature vector, six different DPAs are investigated and compared to find the best one for flash flood detection. Then, the performance of the proposed method is compared with that of a support vector machine (SVM) based classifier. For Hurricane Harvey and Hurricane Irma, the selected method is able to detect 89.00% and 85.00% of flooded points, respectively, with a resolution of 500m × 500m, and the detection accuracy for non-flooded land points is 97.20% and 71.00%, respectively.

This letter presents the validation of an electromagnetic (EM) backscatter model of icebergs at C-band by comparing the performances of target classifiers trained with both modeled and real synthetic aperture radar (SAR) data. Simulated SAR data were obtained in a combination of imaging beam modes and scene parameters to produce 216 simulated Sentinel-1 C-band SAR images. Parameters consisted of Sentinel-1 IW1 (33.1°) and IW3 (43.1°) beam modes with varying wind speed (5 and 10 m/s), wind direction (0°, 45°, and 90°), and target orientation (0°, 45°, and 90°). Simulations were created from an EM SAR simulator called GRECOSAR, which took 3-D profiles of iceberg and ship targets and parameters necessary to closely mimic the real scenes. 3-D models of three icebergs were captured in a field study off the coast of Bonavista, Newfoundland, and Labrador, Canada in June 2017. Three generic ship models were sourced from an online inventory and scaled to a size equivalent to that of the iceberg targets. Real SAR image data were drawn from in-house data set collected from a complementary research program. Classifiers including support vector machine (SVM), Random Forest (RanFor), k-nearest neighbor (kNN), and neural network (NN) were trained with targets from modeled SAR data and then gradually mixed with real SAR data. Target classifier performance from the modeled target data was shown to be similar to classifiers trained entirely from real SAR data. The similarity in accuracy provides an indication of the validity of the modeled SAR data for this specific application.

The inland water bodies are critical components of ecosystems and hydrologic cycles. Thus, the water extent data are crucially important for hydrological and ecological studies. Due to its high temporal resolution, the Cyclone Global Navigation Satellite System (CYGNSS) has the potential for real-time inland water monitoring. In this letter, a high-resolution machine learning (ML) method for detecting inland water content using the CYGNSS data is implemented via the random undersampling boosted (RUSBoost) algorithm. The CYGNSS data of the year 2018 over the Congo and Amazon basins are gridded into $0.01^{\circ }\, \times \, 0.01^{\circ }$ cells. The RUSBoost-based classifier is trained and tested with the CYGNSS data over the Congo basin. The data of the Amazon basin that is unknown to the classifier are then used for further evaluation. By only using the observables extracted from the CYGNSS data, the proposed technique is able to detect 95.4% and 93.3% of the water bodies over the Congo and Amazon basins, respectively. The performance of the RUSBoost-based classifier is also compared with an image processing-based inland water detection method. For the Congo and Amazon basins, the RUSBoost-based classifier has a 3.9% and 14.2% higher water detection accuracy, respectively.

This article presents an electromagnetic backscatter model of iceberg and compares the modeled scattering behavior with C-band RADARSAT-2 synthetic aperture radar (SAR) images. It also explores iceberg SAR signature variability over various ocean parameters. Three-dimensional (3-D) profiles of icebergs were captured in a field study off the coast of Bonavista, NL, Canada, in June 2017 at the time of an SAR satellite overpass. The 3-D profiles were captured from a vessel, using a LiDAR and multibeam sonar. The SAR image and 3-D profiles were captured within hours of one another. Simulated SAR images of the icebergs were generated in a simulator called GRECOSAR with the satellite, target orientation, and ocean parameters that closely mimic the real SAR scene. A new ocean model was introduced to model an ocean backscatter at satellites' lower incidence angle beam mode. Comparison between real and simulated SAR images of the icebergs shows good agreement in terms of SAR signature, total radar cross section, and polarimetric decomposition. Wind direction was varied over 90° extent to observe icebergs' backscatter variability in the simulator. Furthermore, simulated SAR images were generated for low and high wind conditions. Our study finds that the macrostructure of the melt iceberg dominates its polarimetric behavior of its backscatter. Large variability of iceberg SAR signature over varying ocean parameters was also observed. A mathematical model that considers the melting condition of iceberg suggested that significant backscattering can reflect from top surface when the melt water layer could be as little as 0.1 mm.

In this paper, the advantages of using an interferometric method for detecting and characterizing icebergs in sea ice were demonstrated. Iceberg topography was analyzed using single-pass TanDEM-X interferometric synthetic aperture radar (InSAR) data. Multiple InSAR data sets in bistatic mode were acquired over icebergs in sea ice in the Arctic region. InSAR processing was used to extract 3-D elevation information of the sea ice surface. The results firmly demonstrate the capability of TanDEM-X data to characterize the shape of icebergs. Very high resolution (VHR) optical satellite data were collected by Pleiades 1A over the same area to derive digital elevation models (DEMs) of ice features for validation. The accuracy of the extracted topography over icebergs was evaluated by comparing InSAR and optical DEMs. The quantitative comparison demonstrated good correspondence between InSAR and optical DEMs with root-mean-square value values of 2.2 m for icebergs and 0.6 m for sea ice, respectively. Using DEMs derived from VHR optical imagery, it was possible to calculate receiver operating characteristics (ROC) for detecting icebergs in sea using InSAR. The resulting ROC analysis illustrates a good detection performance.

The classification of objects and man-made structures on the ocean surface using synthetic aperture radar (SAR) imagery finds an important use in monitoring for icebergs and sea ice. Convolutional Neural Network (CNN) method can be employed to effectively classify objects imaged through SAR data. In this paper, the CNN performance is evaluated when three different preprocessing procedures are applied to the SAR image data to prepare the CNN's input data. Segmentation or normalization algorithms are implemented in the three procedures. Experimental results demonstrate an improvement over unsegmented and un-normalized images. Performance metrics for all three methods are approximately 94%, indicating that while some image preprocessing is required to achieve higher performance, the CNN tested is robust to the noise present in the SAR images used.

C-band synthetic aperture radar (SAR) is widely used for sea ice monitoring and operational activities. The RADARSAT Constellation Mission (RCM), with its anticipated launch in 2018, will provide hybrid compact polarimetric (CP) C-band SAR data offering near-polarimetric capabilities at large image acquisition widths suitable for achieving operational and scientific objectives in the Arctic. Although C-band SAR is effective for sea ice monitoring, it is difficult to implement during advanced melt, when the sea ice cover is melting and covered by melt ponds. Ice type separability during winter (pre-melt) and advanced melt conditions was assessed using Kolmogorov–Smirnov statistical separability analyses and Support Vector Machine supervised classifications of RCM parameters simulated from 2 winter and 2 advanced melt RADARSAT-2 scenes. Through a detailed analysis of the 2 advanced melt scenes, it was found that the steep incidence angle (22.3–24.2°) simulated RCM CP parameters provide improved ice type separability during the advanced melt period compared with shallow incidence angles (39.6–42.2°). With respect to classification, an overall accuracy of 77.06% was found for a scene comprising first-year and multiyear ice types, and a higher overall accuracy of 85.91% was achieved by including gray level co-occurrence matrix parameters in the classification.

Satellite monitoring offers unique advantages to the industry in meeting the objectives of managing third-party encroachment to mitigate the potential of mechanical damage. Satellite monitoring of third-party encroachment involves a persistent acquisition of satellite imagery over a pipeline right-of-way (ROW), combined with computerized change detection to identify potentially hazardous activities. Monitoring using satellite synthetic aperture radar (SAR) provides an all-weather day or night monitoring of a specific geographic location. This monitoring service can be enhanced with third-party information to increase the confidence in targets detected within satellite imagery. This information can also be used to reduce false positives. A simplistic example of this would be to use road location information overlaid with target information. A target found on a road, such as a tractor-trailer rolling down a highway, represents a small risk to a pipeline and subsequently can be given a lower risk or even be removed as a threat altogether. On the other hand, a large vehicle in a field near a pipeline and not on a road may represent a higher risk to a pipeline. The higher confidence data in-turn allows pipeline integrity operations staff to focus on the higher probability targets, saving time and resources, while maintaining safety standards. This concept has been implemented in the form of a Bayesian Belief Network (BBN) Decision Support System (DSS) that integrates with CalPoly's Representational State Transfer Access for Pipeline Integrity Database (RAPID). RAPID houses multiple data sources such as roads, utilities, agriculture, and construction information to increase target confidence. Both the BBN-DSS and RAPID were developed under the same DOT Cooperative Agreement (OASRTRS-14-H-CAL).

A multi-category numerical sea ice model CICE was used along with data assimilation to derive sea ice parameters in the region of Baffin Bay and Labrador Sea. The assimilation of ice concentration was performed using the data derived from the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2). The model uses a mixed-layer slab ocean parameterization to compute the sea surface temperature (SST) and thereby to compute the freezing and melting potential of ice. The data from Advanced Very High Resolution Radiometer (AVHRR-only optimum interpolation analysis) were used to assimilate SST. The modelled ice parameters including concentration, ice thickness, freeboard and keel depth were compared with parameters estimated from remote-sensing data. The ice thickness estimated from the model was compared with the measurements derived from Soil Moisture Ocean Salinity – Microwave Imaging Radiometer using Aperture Synthesis (SMOS–MIRAS). The model freeboard estimates were compared with the freeboard measurements derived from CryoSat2. The ice concentration, thickness and freeboard estimates from the model assimilated with both ice concentration and SST were found to be within the uncertainty in the observation except during March. The model-estimated draft was compared with the measurements from an upward-looking sonar (ULS) deployed in the Labrador Sea (near Makkovik Bank). The difference between modelled draft and ULS measurements estimated from the model was found to be within 10 cm. The keel depth measurements from the ULS instruments were compared to the estimates from the model to retrieve a relationship between the ridge height and keel depth.

Use of machine learning to develop algorithms for distinguishing iceberg and vessel targets requires large validated data sets that are often costly, time consuming and, in some cases, inaccessible. Generating electromagnetic (EM) backscatter models of iceberg and ship targets can be a vital step in developing a robust iceberg/ship classification algorithm. In this work, EM backscatter models for icebergs are developed using an EM backscatter modelling tool called GRECOSAR and compared with ground truth data. The imaging scene consists of iceberg targets surrounded by the ocean surface. The 3D computer aided design models of the icebergs were obtained using LiDAR and multi-beam sonar data collected during a field program off the coast of Salvage, Newfoundland and Labrador, Canada. While profiling the iceberg targets, a synthetic aperture radar (SAR) image from Sentinel-1A was captured and compared with the simulated SAR images. Comparisons made in terms of total radar cross section (TRCS) and the SAR signature of the targets generally indicate credible simulations. Simulated SAR images were generated at low and high dielectric conditions to mimic cold and melt iceberg surfaces. Variability of the TRCS and morphology as a function of target orientation highlights the usefulness of EM modelling in developing robust iceberg/ship classifiers.

Drifting icebergs can threaten navigation and marine operations and are prevalent in a number of regions that have active oil and gas exploration and development. Satellite synthetic aperture radar (SAR) is naturally applicable to map and monitor icebergs and sea ice due its ability to capture images day or night, as well as through cloud, fog and various wind conditions. There are several notable examples of its use to support operations, including Grand Banks, Barents Sea, offshore Greenland and Kara Sea. New constellations of satellites and the increasing volume of satellite data becoming available present a new paradigm for ice surveillance, in terms of persistence, reliability and cost. To fully extract the value of the data from these constellations, automation and cloud-based processing must be implemented. This will allow more timely and efficient processing, lowering monitoring costs by at least an order of magnitude. The increase in data persistence and processing capability allows large regions to be monitored daily for ice incursions, thus increasing safety and efficiency during offshore operations in those regions. The process of automating SAR-based iceberg surveillance involves creating a process flow that is robust and requires limited human intervention. The process flow involves land-masking, target detection, target discrimination and product dissemination. Land masking involves the removal of high-clutter land from the imagery to eliminate false detection from these locations. Target detection usually involves an adaptive threshold to separate true targets from the background ocean clutter. A constant false alarm rate (CFAR) is a standard technique used in radar image processing for this purpose. Target discrimination involves an examination of the distinct features of a target to determine if they match the features of icebergs, vessels or other ‘false alarms’ (e.g., marine wildlife, clutter). The final stage is the production of an output surveillance product, which can be a standard iceberg chart (e.g., MANICE) or something that can be ingested into a GIS system (e.g., ESRI shapefile, Google KML). The target discrimination phase is one of the most important phases because it provides feedback to operations about the presence of targets of interest (icebergs and vessels). The authors have used computer vision techniques successfully to train target classifiers. Standard techniques usually result in classifier accuracies of between 85%-95%, depending on the resolution of the SAR (higher resolutions produce more accurate results) and the availability of multiple polarizations. To see if new machine learning techniques could be applied to increase classifier accuracy, a dataset of 5000 ship and iceberg targets were extracted from Sentinel-1 multi-channel data (HH,HV). The images were collected in several regions (Greenland, Grand Banks, and Strait of Gibraltar). Validation either came by way of supporting information from the offshore operations, or was inferred by location. An online machine learning competition was hosted by Kaggle, a company that conducts online competitions on behalf of their clients. The detection data were made available by Kaggle to the broad internet community. Kaggle has a loyal following of data scientists who regularly participate in Kaggle competitions. The competition was hosted over a three-month period; over 3300 teams participated in the competition. The competition produced an improved classifier over standard computer vision techniques; the top three competitors had 4-5 stage classifiers that increased classification accuracy by approximately 5%.